2 research outputs found
Swarming around Shellfish Larvae
The collection of wild larvae seed as a source of raw material is a major sub
industry of shellfish aquaculture. To predict when, where and in what
quantities wild seed will be available, it is necessary to track the appearance
and growth of planktonic larvae. One of the most difficult groups to identify,
particularly at the species level are the Bivalvia. This difficulty arises from
the fact that fundamentally all bivalve larvae have a similar shape and colour.
Identification based on gross morphological appearance is limited by the
time-consuming nature of the microscopic examination and by the limited
availability of expertise in this field. Molecular and immunological methods
are also being studied. We describe the application of computational pattern
recognition methods to the automated identification and size analysis of
scallop larvae. For identification, the shape features used are binary
invariant moments; that is, the features are invariant to shift (position
within the image), scale (induced either by growth or differential image
magnification) and rotation. Images of a sample of scallop and non-scallop
larvae covering a range of maturities have been analysed. In order to overcome
the automatic identification, as well as to allow the system to receive new
unknown samples at any moment, a self-organized and unsupervised ant-like
clustering algorithm based on Swarm Intelligence is proposed, followed by
simple k-NNR nearest neighbour classification on the final map. Results achieve
a full recognition rate of 100% under several situations (k =1 or 3).Comment: 11 pages, 4 figures,
http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_53.html, submitted to IbPRIA
2005, Portuga
On Image Filtering, Noise and Morphological Size Intensity Diagrams
In the absence of a pure noise-free image it is hard to define what noise is,
in any original noisy image, and as a consequence also where it is, and in what
amount. In fact, the definition of noise depends largely on our own aim in the
whole image analysis process, and (perhaps more important) in our
self-perception of noise. For instance, when we perceive noise as disconnected
and small it is normal to use MM-ASF filters to treat it. There is two
evidences of this. First, in many instances there is no ideal and pure
noise-free image to compare our filtering process (nothing but our
self-perception of its pure image); second, and related with this first point,
MM transformations that we chose are only based on our self - and perhaps -
fuzzy notion. The present proposal combines the results of two MM filtering
transformations (FT1, FT2) and makes use of some measures and quantitative
relations on their Size/Intensity Diagrams to find the most appropriate noise
removal process. Results can also be used for finding the most appropriate stop
criteria, and the right sequence of MM operators combination on Alternating
Sequential Filters (ASF), if these measures are applied, for instance, on a
Genetic Algorithm's target function.Comment: 9 pages, 4 figures, Author at
http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_25.htm